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Commodity Futures

These entries address investing and trading in commodities and commodity futures as an alternative asset class to equities.

COT Data Predictive for S&P 500 Index?

The zero-sum S&P 500 futures/options market involves three groups of traders: (1) commercial hedgers; (2) non-commercial traders (large speculators); and, (3) non-reportable traders (small or retail speculators) representative of the public. The Commodity Futures Trading Commission (CFTC) collects and publishes aggregate positions (short, long and spread) for each group in a weekly Commitment of Traders (COT) report. CFTC releases reports on Fridays for positions as of the preceding Tuesdays. Are the behaviors of these groups in trading S&P 500 index futures/options reliable indicators of future stock market direction? To investigate, we relate weekly S&P 500 Index futures/options short-long ratios for the three trader categories to S&P 500 Index returns. Using historical weekly COT report data for S&P 500 Index futures and options combined and corresponding weekly dividend-adjusted prices for SPDR S&P 500 (SPY) as a tradable proxy for the index during March 1995 (the earliest available COT data) through early September 2012 (912 weeks), we find that: Keep Reading

Evolution of Commodity Futures Indexes

Does the latest generation of commodity futures indexes, which systematically exploits both backwardation and contango, outperform its predecessors? In her July 2012 paper entitled “Comparing First, Second and Third Generation Commodity Indices”, Joelle Miffre reviews the evolution of commodity futures indexes and assesses the performance of three groups of these indexes: (1) first generation, which are long-only and generally ignore backwardation and contango; (2) second generation, which are also long-only but attempt to mitigate contango while exploiting backwardation; and, (3) third generation, which are long-short to exploit both backwardation and contango. Using monthly levels of 6 first, 23 second and 9 third generation commodity futures indexes from the end of May 2008 through April 2012, she finds that: Keep Reading

Technical Cloning of Hedge Funds with Futures

How effective is technical cloning of hedge funds (attempting to capture a hedge fund’s future returns via a portfolio of liquid assets that empirically replicates the fund’s historical returns)? In the July 2012 version of their paper entitled “Send in the Clones? Hedge Fund Replication Using Futures Contracts”, Nicolas Bollen and Gregg Fisher test whether a replication process can capture some of the benefits of hedge funds (diversification and high Sharpe ratio) while avoiding associated high fees, illiquidity and opacity. They choose one broad and nine strategy-focused hedge fund indexes as targets for replication. They seek to replicate hedge fund index returns with combinations of five fully collateralized futures contracts: U.S. Dollar Index; 10-year T-Note; Gold; Crude Oil; and, S&P 500 Index. Fully collateralized means that they cover potential exposure (positive or negative) with cash earning the risk-free rate (one-month LIBOR). Specifically, they set weights for the futures contracts each month based on linear regression of monthly returns for a hedge fund index versus returns for the five futures contracts over a rolling historical window (see the figure below). They calculate futures contract returns based on holding the nearest-to-expiration contract and rolling to the next maturity five days before expiration. While this process could exploit hedge fund index timing of market factors, it cannot capture any idiosyncratic (non-factor) alpha. Using monthly returns for the ten hedge fund indexes and the five futures contract series during January 1994 through December 2011, they find that: Keep Reading

Commodity Futures Investing Updates

How has recent data meshed with seminal research on commodity futures? In the June 2012 version of their paper entitled “Commodity Investing”, Geert Rouwenhorst and Ke Tang review and update research relevant to investing in commodity futures, with trader positions recorded via Commitments of Traders (COT) reports issued by the Commodity Futures Trading Commission monthly during 1986 through 1992, weekly since 1993 and more granularly since 2006. They assume commercial traders are hedging physical commodities and non-commercial traders are speculators. Using monthly prices and and trader positions for 28 commodity futures contract series during 1986 through 2010, they find that: Keep Reading

Short-term VIX Futures Performance

In general, when the U.S. stock market goes down, the S&P 500 volatility index (VIX) goes up. VIX is not investable, but VIX futures are available. Are short-term VIX futures a good way to hedge equity market declines and guard against market blow-ups? To investigate we focus on returns from holding the contract nearest to maturity, rolling to the next nearest on maturity dates. For simplicity, we assume that rolling is frictionless (favorable to futures) and that available capital always matches a round number of futures contracts (no residual cash). Using daily levels of VIX and daily settlement values of all VIX futures series from late March 2004 through late March 2012 (eight years), we find that: Keep Reading

Enhancing Financial Markets Volatility Prediction

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

Enhanced Commodity Indexes

Do strategy-based commodity indexes introduced in recent years offer value to investors? In the February 2012 version of their paper entitled “Strategic and Tactical Roles of Enhanced Commodity Indices”, Georgios Rallis, Joelle Miffre and Ana-Maria Fuertes compare the returns and risks of enhanced long-only commodity indexes that exploit signals based on the time-to-maturity, momentum and term structure to those of two traditional commodity indexes: the Standard & Poor’s Goldman Sachs Commodity Index, and the Dow Jones-UBS Commodity Index. Using daily data for commodity futures contracts spanning October 1988 through November 2008, they find that: Keep Reading

Fading Diversification Value of Commodity Futures?

Can investors rely on the power of commodity futures to diversify equities, or have growth in industrial hedging and general financialization of commodities permanently changed correlations? In the November 2011 version of their paper entitled “Correlation in Commodity Futures and Equity Markets Around the World: Long-Run Trend and Short-Run Fluctuation”, Xiao-Ming Li, Bing Zhang and Zhijie Du explore the question of whether recent increases in commodities-stocks correlations are transitory. Specifically, they decompose these correlations across equity markets worldwide into two components: long-run trend, and short-run deviation-from-trend. They apply a “best practices” dynamic conditional correlation model to estimate time-varying return correlations, with additional tests to detect structural breaks in long-run trends. Using daily levels of the Goldman Sachs Commodity Index (GSCI) to represent commodities and 45 country stock market indexes (24 developed and 21 emerging) during 2000 through 2010, they find that: Keep Reading

Exploiting Idiosyncratic Volatility in Commodity Futures

Can investors exploit idiosyncratic volatility exhibited by commodity futures? In their December 2011 paper entitled “Idiosyncratic Volatility Strategies in Commodity Futures Markets”, Adrian Fernancez-Perez, Ana-Maria Fuertes and Joelle Miffre investigate the usefulness of idiosyncratic volatility as a predictor of commodity futures returns. They define idiosyncratic volatility of commodity futures as return volatility not explained by contemporaneous variation in hedging pressure. They calculate hedging pressure from CFTC Commitments of Traders reports by relating long positions to total positions across trader categories. Return calculations assume: (1) holding the first nearby contract up to one month before maturity and then rolling to the next-nearest contract; (2) trading on a fully collateralized basis, meaning that half of trading capital earns the risk-free rate (three-month Treasury bill yield); and, (3) reporting only returns in excess of the risk-free rate, which averages about 3.3% annually over the sample period. They test all combinations of commodity ranking (whether for idiosyncratic volatility, return momentum or roll return) and portfolio holding intervals of 4, 13, 26 and 52 weeks. They calculate alpha by regressing long-short commodity futures portfolio returns against the same-interval hedging pressure risk premium. Using Friday settlement prices of nearest and second-nearest contracts for 27 commodity futures and weekly hedging pressure data during September 30, 1992 through March 25, 2011, they find that: Keep Reading

How Many Commodity Sectors?

How many commodity sectors are relevant for portfolio diversification planning, and how do their behaviors differ? In their December 2011 paper entitled “How Many Commodity Sectors Are There, and How Do They Behave?”, Geetesh Bhardwaj and Adam Dunsby examine the statistical properties of commodity futures prices to discover natural sectors and investigate how returns for these sectors behave under different market conditions. They estimate commodity futures returns based on continually rolling at the end of each month to a long position in the nearest contract that does not have first notice day or expiration date during the next month. They measure all returns as “excess” relative to the one-month Treasury bill yield. They define economic expansions and recessions based on National Bureau of Economic Research (NBER) business cycle dates. They define extreme conditions for economic conditions and the stock market as 5% tails. Using monthly futures-only returns for May 1990 through September 2011 and spot returns as available to extend price histories back through the 1970s for 25 individual commodities, monthly returns for stocks (S&P 500 Index), U.S. bond indexes and the U.S. dollar index and several contemporaneous economic measurements, they find that: Keep Reading

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